Proceedings Article10.1109/ICASSP.2019.8682785
Sparse Bayesian Learning for Robust PCA
Jing Liu,Yacong Ding,Bhaskar D. Rao +2 more
- 10 May 2019
- pp 4883-4887
6
TL;DR: A concise Sparse Bayesian Learning (SBL) method that has minimum assumptions and effectively deals with the crux of the problem, and allows simple and effective Empirical Bayesian inference via MAP-EM.
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Abstract: In this paper, we propose a new Bayesian model to solve the Robust PCA problem - recovering the underlying low-rank matrix and sparse matrix from their noisy compositions. We first derive and analyze a new objective function, which is proven to be equivalent to the fundamental minimizing "rank+sparsity" objective. To solve this objective, we develop a concise Sparse Bayesian Learning (SBL) method that has minimum assumptions and effectively deals with the crux of the problem. The concise modeling allows simple and effective Empirical Bayesian inference via MAP-EM. Simulation studies demonstrate the superiority of the proposed method over the existing state-of-the-art methods. The efficacy of the method is further verified through a text extraction image processing task.
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Citations
Sparse Bayesian Learning for Robust PCA: Algorithms and Analyses
Jing Liu,Bhaskar D. Rao +1 more
TL;DR: This paper derives and analyzes a new objective function, which is proven to be equivalent to the fundamental objective of minimizing the “rank+sparsity”, and develops a concise Sparse Bayesian Learning (SBL) method that makes minimal assumptions and effectively deals with the requirements of the problem.
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Bayesian Dictionary Learning on Robust Tubal Transformed Tensor Factorization.
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•Proceedings Article
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
John Wright,Arvind Ganesh,Shankar R. Rao,Yigang Peng,Yi Ma +4 more
- 07 Dec 2009
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